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All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) Bulletin of Electrical Engineering and Informatics
Abu Owida, Hamza
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Computer Science & IT Electrical & Electronics Engineering Engineering

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Journal : Bulletin of Electrical Engineering and Informatics

 1 
Applications of nanostructured materials for severe acute respiratory syndrome-CoV-2 diagnostic Turab, Nidal M.; Abu Owida, Hamza; Al-Nabulsi, Jamal I.; Alnaimat, Feras
Bulletin of Electrical Engineering and Informatics Vol 13, No 4: August 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i4.7325

Abstract

There is a growing concern that severe acute respiratory syndrome coronavirus 2 (SARS‑CoV‑2) infections will continue to rise, and there is now no safe and effective vaccination available to prevent a pandemic. This has increased the need for rapid, sensitive, and highly selective diagnostic techniques for coronavirus disease (COVID-19) detection to levels never seen before. Researchers are now looking at other biosensing techniques that may be able to detect the COVID-19 infection and stop its spread. According to high sensitivity, and selectivity that could provide real-time results at a reasonable cost, nanomaterial show great promise for quick coronavirus detection. In order to better comprehend the rapid course of the infection and administer more effective treatments, these diagnostic methods can be used for widespread COVID-19 identification. This article summarises the current state of research into nanomaterial-based biosensors for quick SARS‑CoV‑2 diagnosis as well as the prospects for future advancement in this field. This research will be very useful during the COVID-19 epidemic in terms of establishing rules for designing nanostructure materials to deal with the outbreak. In order to predict the spread of the SARS-CoV-2 virus, we investigate the advantages of using nano-structure material and its biosensing applications.
Progress in self-powered medical devices for breathing recording Abu Owida, Hamza; Turab, Nidal; Al-Nabulsi, Jamal I.; Al-Ayyad, Muhammad
Bulletin of Electrical Engineering and Informatics Vol 13, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v13i5.5253

Abstract

Wearable and implantable medical technologies are increasingly being used for the diagnosis, treatment, and prevention of illnesses and other health concerns. One's respiration can be monitored using any number of different biosensors and tracking devices. Self-powered sensors, for example, have a reduced total cost, are easy to prepare, have a high degree of design-ability, and are available in a number of different forms when compared to other types of sensors. The mechanical energy stored in the respiratory system could be converted into electrical energy by using airflow to operate self-powered sensors. Self-recharging sensors and systems are now in development to make home health monitoring and diagnosis more practical. There has not been a lot of study devoted to the models of respiratory sickness or the output signals that connect with them. Thus, investigating the character of their bond is not only difficult but also crucial. This article examined the theory behind self-powered breathing sensors and systems, as well as their output characteristics, detection indices, and other cutting-edge developments. To help communicate knowledge to other academics working in this field and interested in this topic, we also explored the challenges and potential benefits of autonomous sensors.
Progression of polymeric nanostructured fibres for pharmaceutical applications Abu Owida, Hamza; I. Al-Nabulsi, Jamal; M. Turab, Nidal; Al-Ayyad, Muhammad; Alazaidah, Raed; Alshdaifat, Nawaf
Bulletin of Electrical Engineering and Informatics Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i1.7315

Abstract

Electrospinning has emerged as a simple and cost-effective technique for producing polymer nanofibers, offering a versatile approach for creating nanostructured fibers from a wide range of polymer materials. The pharmaceutical field has particularly welcomed the advent of electrospun nanofibers, as they hold immense potential for revolutionizing drug delivery systems. The recent surge of interest in electrospun nanofibers can be attributed to their unique characteristics, including elasticity and biocompatibility, which make them highly suitable for various biomedical applications. By incorporating functional ingredients into blends of nanostructured fibers, the capabilities and reliability of drug delivery devices have been significantly enhanced. This review aims to provide a comprehensive summary of recent research endeavors focusing on the concept of nanofibrous mesh and its multifaceted applications. With an emphasis on the simplicity of fabrication and the virtually limitless combinations of materials achievable through this approach, nanofibrous meshes hold the promise of transforming specific treatment modalities. By streamlining the delivery of therapeutic agents and offering enhanced control over drug release kinetics, nanofibrous meshes may herald a new era in targeted and personalized medicine.
Advancement in self-powered implantable medical systems Abu Owida, Hamza; Al-Nabulsi, Jamal; Turab, Nidal; Al-Ayyad, Muhammad; Al Hawamdeh, Nour; Alshdaifat, Nawaf
Bulletin of Electrical Engineering and Informatics Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i1.5881

Abstract

Many different elements of patient monitoring and treatment can be supported by implantable devices, which have proven to be extremely reliable and efficient in the medical profession. Medical professionals can use the data they collect to better diagnose and treat patients as a result. The devices’ power sources, on the other hand, are battery-based, which introduces a slew of issues. As part of this review, we explore the use of harvesters in implanted devices and evaluate various materials and procedures and look at how new and improved circuits can enable the harvesters to sustain medical devices.
Advancements in machine learning techniques for precise detection and classification of lung cancer Abu Owida, Hamza; Arabiat, Areen; Al-Ayyad, Muhammad; Altayeb, Muneera
Bulletin of Electrical Engineering and Informatics Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i6.10527

Abstract

Lung cancer remains one of the most prevalent and lethal malignancies worldwide, necessitating early detection and accurate classification for effective treatment. In this work, we present a unique machine learning (ML) model that uses medical imaging data to detect and classify lung cancer. Utilizing a dataset of 613 images which obtained from Kaggle, our model combines sophisticated feature extraction methods with three essential algorithms: AdaBoost, stochastic gradient descent (SGD), and random forest (RF). Orange3 data mining software was used to classify the model after it was preprocessed and features were extracted using MATLAB. Nonetheless, the model showed good performance in identifying lung cancer lesions in four different categories: squamous cell carcinoma, big cell carcinoma, adenocarcinoma, and normal. With an accuracy of 0.998 and an AUC range of 1.000, AdaBoost notably produced the best results. Overall, ensemble ML techniques demonstrated notable benefits over single classifiers, indicating its potential to aid in the creation of accurate instruments for the diagnosis of lung cancer in its early stages.
Co-Authors Abuowaida, Suhaila Al Hawamdeh, Nour Al Nabulsi, Jamal Al Sharah, Ashraf Al-Ayyad, Muhammad Al-Nabulsi, Jamal Al-Nabulsi, Jamal I. Alazaidah, Raed Alhaj, Mohammad Ali, Ali Mohd AlMahadin, Ghayth Alnaimat, Feras Alshdaifat, Nawaf Altayeb, Muneera Arabiat, Areen Hassan, Mohammad I. Al-Nabulsi, Jamal M. Turab, Nidal R. Hassan, Mohammad Sharadqeh, Ahmad Turab, Nidal Turab, Nidal M.